PIPELINED ON-LINE BACK-PROPAGATION TRAINING OF AN ARTIFICIAL NEURAL NETWORK ON A PARALLEL MULTIPROCESSOR SYSTEM
Tiago Mendonça Silva, Antônio P. Braga, Wilian Soares Lacerda · 2016
This work presents an on-chip learning of artificial neural networks in a FPGA multiprocessor system, where each neuron is implemented in a soft-core processor. In order to take maximum advantage of the distributed architecture, a pipelined version of the on-line back-propagation algorithm is used, providing a high degree of parallelism between neuron layers and, hence, a higher speed-up in relation to a sequential implementation.